This project proposes to examine spatial and temporal linkages between human population and the environment in the Wolong Nature Reserve in China. Wolong is the largest reserve for conserving the world-famous endangered giant pandas. It also has more than 4000 local residents. The human population is organized around households (942 in 1998), which traditionally included several generations living together, but this tradition is being broken up. Since 1975, Wolong's human population has grown 66 percent, but the number of households has increased 115 percent. Each household garners resources needed to live, particularly fuelwood for cooking and heating, from the surrounding landscape. In this study, we view population-environment interactions as the interrelationships among five major components: human population, forests, giant panda habitats, socioeconomic and institutional factors, and government policies. Forests and giant panda habitats represent the environment, whereas socioeconomic and institutional contextual factors and government policies influence how human population and the environment interact with each other. Fuelwood consumption by local residents is now the single most important human factor affecting forests and subsequently giant panda habitats (forests are an important component of the panda habitats with trees as covers and bamboo as staple food). Thus, we treat fuelwood consumption as the main linkage between human population and the environment. We will take a systems approach to address five interrelated specific aims: (1) to understand human population processes and dynamics, (2) to examine the relationships between fuelwood consumption and household demography, (3) to identify spatial interactions between population and the environment, (4) to analyze reciprocal effects of population and the environment, and (5) to predict long-term spatial dynamics of population-environment interactions under different policy scenarios. To achieve these aims, we will use and integrate extensive household and socioeconomic surveys, interviews with local officials and residents, collection of historical data, field observations and measurements, data from previous and ongoing studies, statistical tools (e.g., event history analysis, multilevel modeling, logistic regression), graph theory and network analysis, spatial technologies (geographic information systems, remote sensing, and global positioning systems), and systems modeling and simulation. The completion of our proposed project will have significant implications for population- environment interaction theories, methods, and applications.